pandas:如何使用多索引运行数据透视?

pandas: how to run a pivot with a multi-index?(pandas:如何使用多索引运行数据透视?)

问题描述

我想在 pandas DataFrame 上运行一个支点，索引是两列，而不是一列.例如，一个字段用于年份，一个用于月份，一个item"字段显示item 1"和item 2"，以及一个带有数值的value"字段.我希望索引为年 + 月.

I would like to run a pivot on a pandas DataFrame, with the index being two columns, not one. For example, one field for the year, one for the month, an 'item' field which shows 'item 1' and 'item 2' and a 'value' field with numerical values. I want the index to be year + month.

我设法使它工作的唯一方法是将两个字段合并为一个，然后再次将它们分开.有没有更好的办法?

The only way I managed to get this to work was to combine the two fields into one, then separate them again. is there a better way?

下面复制的最小代码.非常感谢！

Minimal code copied below. Thanks a lot!

PS 是的，我知道关键字pivot"和multi-index"还有其他问题，但我不明白他们是否/如何帮助我解决这个问题.

PS Yes, I am aware there are other questions with the keywords 'pivot' and 'multi-index', but I did not understand if/how they can help me with this question.

import pandas as pd
import numpy as np

df= pd.DataFrame()
month = np.arange(1, 13)
values1 = np.random.randint(0, 100, 12)
values2 = np.random.randint(200, 300, 12)


df['month'] = np.hstack((month, month))
df['year'] = 2004
df['value'] = np.hstack((values1, values2))
df['item'] = np.hstack((np.repeat('item 1', 12), np.repeat('item 2', 12)))

# This doesn't work: 
# ValueError: Wrong number of items passed 24, placement implies 2
# mypiv = df.pivot(['year', 'month'], 'item', 'value')

# This doesn't work, either:
# df.set_index(['year', 'month'], inplace=True)
# ValueError: cannot label index with a null key
# mypiv = df.pivot(columns='item', values='value')

# This below works but is not ideal: 
# I have to first concatenate then separate the fields I need
df['new field'] = df['year'] * 100 + df['month']

mypiv = df.pivot('new field', 'item', 'value').reset_index()
mypiv['year'] = mypiv['new field'].apply( lambda x: int(x) / 100)  
mypiv['month'] = mypiv['new field'] % 100

推荐答案

你可以分组然后unstack.

You can group and then unstack.

>>> df.groupby(['year', 'month', 'item'])['value'].sum().unstack('item')
item        item 1  item 2
year month                
2004 1          33     250
     2          44     224
     3          41     268
     4          29     232
     5          57     252
     6          61     255
     7          28     254
     8          15     229
     9          29     258
     10         49     207
     11         36     254
     12         23     209

或者使用pivot_table:

>>> df.pivot_table(
        values='value', 
        index=['year', 'month'], 
        columns='item', 
        aggfunc=np.sum)
item        item 1  item 2
year month                
2004 1          33     250
     2          44     224
     3          41     268
     4          29     232
     5          57     252
     6          61     255
     7          28     254
     8          15     229
     9          29     258
     10         49     207
     11         36     254
     12         23     209

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